In the case that the weather is bad, the visibility and color of an image are usually degraded by the fog in the atmosphere. It is usually needed to improve the quality of images and videos captured in the weather by defogging. The processing of removing the fog in an image is called image defogging. Image defogging is very useful for navigation and surveillance in the case that the weather is bad.
Currently, there are many image defogging methods, in which an image defogging method based on dark channel prior is a method having the best effect. The dark channel prior is obtained by making statistics on outdoor non-foggy images, i.e. most non-sky local areas in an outdoor non-foggy image have a pixel, the intensity value of at least one color channel of which is very low (usually close to 0). A defogging model established by utilizing the dark channel prior can directly estimate the thickness of the fog, and can restore a fogging image to a high quality image after removing the interference of the fog (called defogged image for short).
In the image defogging method based on dark channel prior, the intensity value J of an input fogging image is solved by using the intensity value I, the air light value A, and the transmission map t of the fogging image. In a traditional image defogging method based on dark channel prior, it is usually needed to optimize the transmission map by soft matting. However, the processing of soft matting needs very complex computation and thus is difficult to implement in real time.